Can Sentiment Analysis Decode Cross-Cultural Social Media?

Can sentiment analysis shed light on cross-cultural social-media use?

Beyond use: What tools can help measure cross-cultural social-platform expansion and cross-cultural networks?

I like these questions. They originate with writer and researcher Lydia Laurenson, who contacted me after coming across a conference I organize, the Sentiment Analysis Symposium. (I did rephrase them a bit.) They recognize that sentiment analysis, which seeks to classify and quantify opinion, emotion, and connection, can help decode (her label:) social’s “cultural dimensions.” (If you have your own references or thoughts to share, please contact Lydia. Her site is journalismforbrands.com and she’s on Twitter at @lydialaurenson.)

I’m going to riff on these questions, by which I mean, I’m going to both interpret the idea of cross-culture social media and explore responses.

Differentiation

Let’s start with an analysis of the questions, observing that they rightly differentiate social-media use from platforms from networks — content from channel from connections.

Social networks link individuals and organizations. They have directionality — I may follow you and see your messages, even though you don’t follow me — and temporality in that connections change over time. Social networks depend on but transcend platforms. The network is you and me (and Justin Bieber) and not the particular channel we happen to use to communicate.

Facebook, Twitter, Pinterest, Sina Weibo, VKontakte: These are platforms, channels. They support activities: People join them (and dozens of others), create profiles, make connections, and post and consume others’ content. And while my non-friend Justin and I are both on a diversity of platforms — some of the ones I list above, and others — with profiles tailored to the character of each platform, our networks span platforms.

Finally, social-media use covers our actual posting behaviors and the content we post. The more interesting forms of social-media measurement study use — computed values such as advocacy and engagement — the active utilization of connections, which are of little value when unused.

Multi-Cultural and Cross-Cultural

Each and every social platform is stylistically mono-cultural. Each is designed for certain types of content and certain activities and each encourages, per the infamous Social Media and Donuts, a certain style of use. Certain platforms, for instance Sina Weibo and VKontact, are largely mono-lingual or appeal only to a few language groups. But keeping in mind the platform/network/usage differentiation, we do observe that any platform that’s large enough will host a culturally diverse collection of users and content.

Social-media users share and interact with whoever’s interested, whether neighbors, family, a professional audience, or listeners, whether brands or the NSA. Culture and connection derives from the content: If my tweet

“Facial Analytics: What Are You Smiling at?” asks @M_Steinhart in @AllAnalytics, regarding #SAS14 prez by Jacob Whitehill of @EmotientInc

LinkedIn InMap clustering my connections: Each of us is a bridge spanning cultures.

I’m not the only one who’s multi-cultural in the sense of having networks that extend to different audiences. I have multiple Twitter accounts, which I use to reach non-overlapping audiences, but on LinkedIn, my one account connects me to several different audience segments (as seen in the colored clusters in the InMap at right).

In a sense, each of us is a cross-network, cross-cultural bridge.

Organizations that use social media typically seek to reach and engage individuals from many cultures, using media and languages suited to reach and appeal to those cultures. Arguably, trying to communicate ideas and sell products, developed in/for one market, reflecting that market’s values and lifestyles, in/to another market, is a cross-cultural endeavor.

Sentiment as a Measurement Dimension

In order to evaluate cross-cultural social-media use, we first have to figure out how to measure cultural dimensions. This is a technology question given the premises that a) cultural elements are communicated or created via (online and) social media, b) cultural elements can be decoded from social-media postings and behaviors, and c) automated analysis allows you to systematize decoding or even uncover dimensions not before apparent. Natural language processing (NLP) technology is an answer, given the common notion that language and culture are tightly linked. (Search on Sapir-Whorf.)

Who is developing measures of cultural dimensions and using automated text-analysis technologies to compute them? Arguably, anyone who is doing sentiment analysis is measuring a cultural dimension, whether that work targets a business need — customer service, market research, financial-market trading strategy, something else — or analyzes depression and suicidal-mood indicators, or is applied for counter-terrorism.

We look to understand nuanced word senses, for example, the difference in meaning of “thin wallet” and “wallet is thin.” The first is possibly a product search, indicating buying intent. The second is an expression that says, I’m short on cash and unlikely to buy something. The user profile, wording, and social setting provide context for interpretation, and the needs of the person or organization doing the analysis determine the data treatment. All of this is cultural interpretation, even though a business analyst would never call it that.

Roadmap and Milestones

Who is focusing on the sort of text interpretations most aligned with the idea of measuring cultural dimensions? I’ll name two people I know, and I invite you to let me (and Lydia Laurenson) know about other work. My two are:

Jason Baldridge, who teaches computational linguistics at the Univ of Texas and is co-founder of People Pattern, which “dissects & classifies your audience(s) across demographic, psychographic, sentiment & intent to identify and extend vital personas,” among other functions.

There’s plenty of other work out there along the same lines. Consider one other example, Kanjoya, commercializing Stanford University research, modeling “everything from traditional English language usage to social conversations riddled with emoticons, colloquialisms, and slang… the different meanings words can have based on their topical context, as well as how language varies across age, gender, and even geography.”

The understanding of idiom is key, but I’d venture that the vast majority of work relates to cultural elements expressed in a single language. There isn’t much technology out there (that I know of) for cross/multi-cultural analysis. A direction to explore, however, would be automated machine translation, which to be accurate, must deal with idiom that can’t be translated by simply translating words and syntax.

Sorry, Google, Translate’s rendering of the expression “my wallet is thin” into the French “mon portefeuille est mince” is not idiomatically correct. Nonetheless, advances in machine learning almost guarantee that translation will improve, in tandem with the many other promising “decoding” technologies that have emerged in research and industry settings. NLP, stylistic analysis and profile extraction, and contextual interpretation, along with the (nascent) ability to map idiom and other cultural elements, will facilitate cross-cultural analysis. Sentiment analysis points the way.